Tableau Data Analysis and AI Insights Training Course

Participants can prepare, reshape and validate data for reliable Tableau analysis.

• Profile nulls, duplicates, outliers and inconsistent categories using Tableau Prep
• Apply pivots, splits, joins and unions to reshape source files
• Build calculated cleaning fields with IF, CASE and date functions
• Aggregate transaction-level data to the required analytical grain
• Schedule Prep flows and trace output lineage through published data sources
• Validate record counts, reconciliation totals and join cardinality before publication

Participants can create and test Tableau calculations at the correct level of aggregation.

• Differentiate row-level, aggregate, table calculation and LOD calculation scopes
• Create calculated fields using conditional, string, date and numeric functions
• FIXED LOD expressions define customer, product and period-level measures
• INCLUDE and EXCLUDE LOD expressions preserve intended view granularity
• Table calculations compute running totals, moving averages and percent differences
• Test calculation logic against known records and finance-control totals

Participants can apply appropriate Tableau analytical methods to investigate business performance.

• Select comparison baselines, targets and thresholds before building views
• Trend analysis separates seasonality, anomalies and structural performance changes
• Use reference lines, bands and distributions to contextualise variation
• Apply Pareto analysis to prioritise contributing categories and causes
• Segmentation combines customer, product, geography and channel dimensions
• Forecasting options, confidence intervals and data requirements inform planning use

Participants can select and design Tableau visualisations that communicate evidence clearly.

• Match bar, line, scatter, map and highlight-table charts to questions
• Apply preattentive attributes: position, length, colour, size and enclosure
• Design accessible colour palettes, labels and tooltips for mixed audiences
• Remove chartjunk, misleading axes and unnecessary precision from executive views
• Build KPI tiles with targets, variance and directional context
• Use visual hierarchy to separate monitoring signals from explanatory detail

Participants can build interactive Tableau dashboards for different users and devices.

• Plan dashboard layouts around decisions, users, frequency and screen constraints
• Create actions for filtering, highlighting, navigation and parameter-driven analysis
• Parameters switch measures, scenarios, dates and calculation logic interactively
• Set context filters and order of operations for correct results
• Design device layouts for desktop, tablet and mobile consumption
• Test dashboard load time, usability and interpretation with representative users

Participants can use Tableau AI features while validating findings and avoiding unsupported conclusions.

• Tableau Pulse metrics surface personalised changes, trends and explanatory context
• Explain Data identifies statistically relevant factors behind marks and outliers
• Tableau Agent prompts assist calculations, visualisations and data exploration
• Write analytical prompts specifying measure, population, period and comparison baseline
• Validate AI-generated insights against source data, calculation definitions and business knowledge
• Distinguish correlation, statistical explanation and causal claims in AI-assisted analysis

Participants can apply governance controls to Tableau data, metrics, access and AI outputs.

• Define certified data sources, owners, refresh schedules and quality controls
• Apply role-based permissions, row-level security and project-level content access
• Document metric definitions, calculation assumptions and approved analytical interpretations
• Assess AI outputs for confidentiality, bias, accuracy and auditability risks
• Use Tableau Catalog lineage to investigate upstream fields and dependencies
• Establish review checkpoints before distributing high-consequence dashboards and alerts

Participants can publish and maintain Tableau content that remains reliable after release.

• Publish workbooks, data sources and flows using governed project structures
• Configure extract refreshes, subscriptions, data-driven alerts and metric notifications
• Monitor failed refreshes, broken fields and performance regressions after release
• Apply performance recording to isolate slow queries, calculations and rendering
• Reduce workbook load time through extract design and query optimisation
• Create release notes, ownership records and support handover documentation

Participants can create a practical plan for improving Tableau reporting and AI insight use.

• Translate management questions into metrics, views, alerts and decision routines
• Prioritise dashboard enhancements using impact, effort, risk and adoption criteria
• Build an AI insight validation checklist for recurring analytical decisions
• Set adoption measures: active users, subscriptions, response time and decision outcomes
• Plan a 30-day Tableau improvement backlog with named owners
• Present evidence-based recommendations, limitations and next actions to stakeholders

You should be comfortable working with spreadsheets or structured data and interpreting common business measures. Basic Tableau familiarity is helpful, but the course does not assume advanced calculations, dashboard development, SQL or coding.

Bring a laptop if attending in person, together with any permitted sample data, metric definitions or reporting examples from your workplace. Tableau access is provided for course activities where required; do not bring confidential or restricted data unless your organisation has approved its use.

It is designed for analysts, BI professionals, reporting specialists, performance managers and subject-matter professionals who use data to support decisions. It suits people moving from basic Tableau reporting towards governed, interactive analysis and AI-assisted investigation.

This course assumes participants can work with business data and focuses on calculations, analytical methods, governed publishing and Tableau AI capabilities. It teaches Tableau-specific features rather than Power BI, and gives more attention to validation and operational use than introductory chart-building courses.

Participants can use the dashboard design brief, calculation patterns, quality checks and publishing checklist on active reporting work. The 30-day improvement backlog helps identify a suitable dashboard, data source or AI insight process to improve after the course.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. Time is allocated to data preparation, calculations, analytics, visual design, dashboards, AI insights, governance, publishing and workplace application planning.